{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests                                                                      \n",
    "from bs4 import BeautifulSoup \n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 310\n",
      "过滤后蒙阴县台区数: 33\n",
      "过滤后蒙阴县用户数: 138\n"
     ]
    }
   ],
   "source": [
    "# 过滤后的用户\n",
    "black=pd.read_csv('./data/用户黑名单.txt')\n",
    "black_ = black['mdid'].drop_duplicates()\n",
    "# 过滤后的台区\n",
    "sbbm = np.loadtxt('./data/37413_1.txt', dtype = str)\n",
    "\n",
    "#所有用户发电情况(311个)\n",
    "#with open(\"./gfdata/data/20211109/202111092300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211110/202111101900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211111/202111111900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211113/202111132200.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211114/202111141900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211115/202111151900.txt\",encoding='utf-8') as f:\n",
    "with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "    content = f.read().replace(\"\\n\",\"\")\n",
    "    content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)\n",
    "print(\"总用户数:\",len(json_data['res']))\n",
    "\n",
    "\n",
    "\n",
    "#解析json\n",
    "#异常值处理\n",
    "#过滤台区\n",
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl'])*10 if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] = float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    data['dwfd'] = data['drfdl']/data['gfrl'] if data['gfrl']!=0 else 0\n",
    "    if((data['sbbm'] in sbbm) and (data['ycsb'] not in black_.to_list())):\n",
    "        data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)\n",
    "print(\"过滤后蒙阴县用户数:\",len(gfdata))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.光伏容量与发电量\n",
    "将gfrl数值放大绘图，与发电量 \"趋势\" 基本一致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['gfrl','drfdl'],figsize=(18,3),kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "读取: ./gfdata/data/20211113/202111130100.txt\n",
      "读取: ./gfdata/data/20211113/202111130200.txt\n",
      "读取: ./gfdata/data/20211113/202111130300.txt\n",
      "读取: ./gfdata/data/20211113/202111130400.txt\n",
      "读取: ./gfdata/data/20211113/202111130500.txt\n",
      "读取: ./gfdata/data/20211113/202111130600.txt\n",
      "读取: ./gfdata/data/20211113/202111130700.txt\n",
      "读取: ./gfdata/data/20211113/202111130800.txt\n",
      "读取: ./gfdata/data/20211113/202111130900.txt\n",
      "读取: ./gfdata/data/20211113/202111131000.txt\n",
      "读取: ./gfdata/data/20211113/202111131100.txt\n",
      "读取: ./gfdata/data/20211113/202111131200.txt\n",
      "读取: ./gfdata/data/20211113/202111131300.txt\n",
      "读取: ./gfdata/data/20211113/202111131400.txt\n",
      "读取: ./gfdata/data/20211113/202111131500.txt\n",
      "读取: ./gfdata/data/20211113/202111131600.txt\n",
      "读取: ./gfdata/data/20211113/202111131700.txt\n",
      "读取: ./gfdata/data/20211113/202111131800.txt\n",
      "读取: ./gfdata/data/20211113/202111131900.txt\n",
      "读取: ./gfdata/data/20211113/202111132000.txt\n",
      "读取: ./gfdata/data/20211113/202111132100.txt\n",
      "读取: ./gfdata/data/20211113/202111132200.txt\n",
      "读取: ./gfdata/data/20211113/202111132300.txt\n"
     ]
    }
   ],
   "source": [
    "content = \"\"\n",
    "for i in range(1,24):\n",
    "    if i < 10:\n",
    "        path = \"./gfdata/data/20211113/202111130\" + str(i) + \"00.txt\"\n",
    "    else:\n",
    "        path = \"./gfdata/data/20211113/20211113\" + str(i) + \"00.txt\"\n",
    "    print(\"读取:\",path)\n",
    "    with open(path,encoding='utf-8') as f:\n",
    "        content = content + f.read().replace(\"\\n\",\"\")[0:-1] + \",\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 7145\n"
     ]
    }
   ],
   "source": [
    "content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    \n",
    "    data['p'] = (-1)*float(json_data['res'][i]['result']['Data']['p']) if json_data['res'][i]['result']['Data']['p']!=None else 0\n",
    "    data['q'] = float(json_data['res'][i]['result']['Data']['q']) if json_data['res'][i]['result']['Data']['q']!=None else 0\n",
    "    data['szgl'] = (2)*float(json_data['res'][i]['result']['Data']['szgl']) if json_data['res'][i]['result']['Data']['szgl']!=None else 0\n",
    "    \n",
    "    \n",
    "    data['glys'] = json_data['res'][i]['result']['Data']['glys']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl']) if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] =float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    if(data['ycsb']=='202106060285GF'):\n",
    "        data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-dda6c2fce7f0>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gfdata['fdgl'][22] = 0\n",
      "<ipython-input-44-dda6c2fce7f0>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gfdata['fdgl'][0] = gfdata['drfdl'][i] - c\n",
      "<ipython-input-44-dda6c2fce7f0>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gfdata['fdgl'][i-1] = (gfdata['drfdl'][i] - gfdata['drfdl'][i-1])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>szgl</th>\n",
       "      <th>glys</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "      <th>id</th>\n",
       "      <th>fdgl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>10.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>8.08</td>\n",
       "      <td>0.04</td>\n",
       "      <td>16.16</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>10.63</td>\n",
       "      <td>8</td>\n",
       "      <td>36.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>15.85</td>\n",
       "      <td>0.07</td>\n",
       "      <td>31.70</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>47.39</td>\n",
       "      <td>9</td>\n",
       "      <td>50.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>20.57</td>\n",
       "      <td>0.07</td>\n",
       "      <td>41.14</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>98.20</td>\n",
       "      <td>10</td>\n",
       "      <td>67.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>24.10</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>48.20</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>166.04</td>\n",
       "      <td>11</td>\n",
       "      <td>73.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>24.65</td>\n",
       "      <td>0.13</td>\n",
       "      <td>49.30</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>239.99</td>\n",
       "      <td>12</td>\n",
       "      <td>63.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>21.38</td>\n",
       "      <td>0.05</td>\n",
       "      <td>42.76</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>303.90</td>\n",
       "      <td>13</td>\n",
       "      <td>65.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>17.97</td>\n",
       "      <td>0.08</td>\n",
       "      <td>35.94</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>369.41</td>\n",
       "      <td>14</td>\n",
       "      <td>41.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>10.71</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>21.42</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.84</td>\n",
       "      <td>411.36</td>\n",
       "      <td>15</td>\n",
       "      <td>20.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>4.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>8.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>432.03</td>\n",
       "      <td>16</td>\n",
       "      <td>5.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.84</td>\n",
       "      <td>437.64</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sbbm            ycsb yhmc      p     q   szgl  glys   gfrl  \\\n",
       "0   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "1   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "2   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "3   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "4   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "5   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "6   06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "7   06M00000035359934  202106060285GF  杨传友   8.08  0.04  16.16  0.99  14.84   \n",
       "8   06M00000035359934  202106060285GF  杨传友  15.85  0.07  31.70  0.99  14.84   \n",
       "9   06M00000035359934  202106060285GF  杨传友  20.57  0.07  41.14  0.99  14.84   \n",
       "10  06M00000035359934  202106060285GF  杨传友  24.10 -0.18  48.20  0.99  14.84   \n",
       "11  06M00000035359934  202106060285GF  杨传友  24.65  0.13  49.30  0.99  14.84   \n",
       "12  06M00000035359934  202106060285GF  杨传友  21.38  0.05  42.76  1.00  14.84   \n",
       "13  06M00000035359934  202106060285GF  杨传友  17.97  0.08  35.94  0.99  14.84   \n",
       "14  06M00000035359934  202106060285GF  杨传友  10.71 -0.08  21.42  0.99  14.84   \n",
       "15  06M00000035359934  202106060285GF  杨传友   4.45 -0.02   8.90  1.00  14.84   \n",
       "16  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "17  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "18  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "19  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "20  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  None  14.84   \n",
       "21  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "22  06M00000035359934  202106060285GF  杨传友  -0.00  0.00   0.00  1.00  14.84   \n",
       "\n",
       "     drfdl  id   fdgl  \n",
       "0     0.00   1      0  \n",
       "1     0.00   2      0  \n",
       "2     0.00   3      0  \n",
       "3     0.00   4      0  \n",
       "4     0.00   5      0  \n",
       "5     0.00   6      0  \n",
       "6     0.00   7  10.63  \n",
       "7    10.63   8  36.76  \n",
       "8    47.39   9  50.81  \n",
       "9    98.20  10  67.84  \n",
       "10  166.04  11  73.95  \n",
       "11  239.99  12  63.91  \n",
       "12  303.90  13  65.51  \n",
       "13  369.41  14  41.95  \n",
       "14  411.36  15  20.67  \n",
       "15  432.03  16   5.61  \n",
       "16  437.64  17      0  \n",
       "17  437.64  18      0  \n",
       "18  437.64  19      0  \n",
       "19  437.64  20      0  \n",
       "20  437.64  21      0  \n",
       "21  437.64  22      0  \n",
       "22  437.64  23      0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = 0\n",
    "gfdata['fdgl'] = ''\n",
    "gfdata['fdgl'][22] = 0\n",
    "for i in range(0,23):\n",
    "    if i == 0:\n",
    "        gfdata['fdgl'][0] = gfdata['drfdl'][i] - c\n",
    "    else:\n",
    "        gfdata['fdgl'][i-1] = (gfdata['drfdl'][i] - gfdata['drfdl'][i-1])\n",
    "gfdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 发电量0-23点变化趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['drfdl'],figsize=(20,3),kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.发电功率与视在功率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['szgl','fdgl'],figsize=(10,3),kind='line')'p','q','fdgl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.发电功率与P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwAq4YQxUa+52IvG2KknQeggsfx9OpbqdRkRKQQVKxC3f/wvWTIZef4Cm17idRnylx2OQnwNL3nA7iYiUggqUiBu2fAuznoHmAyH5cbfTiC9VbQAtb4Rl78HpI26nEZESUoES8bX0Lc5imdVbwsBR2qYlGPV4HHIz4Yc33U4iIiWkAiXiS1nHCrdpiYAhE7VNS7BKaOpsEr30bchIczuNiJRAkQuUMSbUGLPSGPNl4f1YY8wsY8zWwtsq3ospEgDy8+CTu+D4Hrj5I6ic6HYicVPvPzhXYS58ye0kIlICxRmBegjYeNb9J4E51tpGwJzC+yJyPrOfge1zCrdp6eJ2GnFbXCNo9ytIeU975In4oSIVKGNMbeAa4N2zHh4AjCv8ehww0KPJRALJqgmw5N/QabizpYcIQM8nndO5c//idhIRKaaijkC9BjwBFJz1WDVr7UGAwtuEcz3RGDPcGJNijElJT08vTVYR/7R3mbPeU71k6Pc3t9NIWRJTHbo8AOunwf4VbqcRkWK4aIEyxvQH0qy1y0tyAGvtGGttB2tth/j4+JK8hIj/OnkAJt8GFWvC4HHapkV+qetvoEJV5xSvtW6nEZEiKsoIVDfgOmPMLmAS0NsY8xGQaoypAVB4q0tJRM6WmwWTboWc09qmRc4vsiIkPwE7Fzpz5ETEL1y0QFlrn7LW1rbWJgFDgLnW2tuBz4GfJnMMBaZ7LaWIv7EWPh8JB1bBDe9AQjO3E0lZ1uHXULkuzHoWCgou+uMi4r7SrAP1AtDXGLMV6Ft4X0TA2Sx27cfQ+2loerXbaaSsCysHvf8IqWud/29EpMwrVoGy1s631vYv/PqItfZya22jwtuj3oko4me2zITZz0KLG5wVp0WKouWNUP0S54q8vDNupxGRi9BK5CKelHUMpt4D1VvBgDe1TYsUXUgI9H0OTuyBZWPdTiMiF6ECJeJJy8fBmRNOeYqo4HYa8TcNekP9Xs7q5Nkn3E4jIhegAiXiKfm58OMYSOoBNS5xO434qz7PQtZRZx6diJRZKlAinrLxczi531kYUaSkaraBloNgySg4edDtNCJyHipQIp6yZBTENoBG/dxOIv6u9x+gIA/m/93tJCJyHipQIp6wdxnsT4HO9zmTgUVKI7YedLwLVn4I6VvcTiMi56Df9CKe8MObEFkJWt/idhIJFMm/hfAomPOc20lE5BxUoERK6/he2PA5tBsK5aLdTiOBIioOuj0Em76EPUvdTiMi/0MFSqS0fhzj3HYa7m4OCTxd7oeoBG00LFIGqUCJlMaZDGftp+bXQeVEt9NIoImIgp5Pwp4lsGWG22lE5CwqUCKlsWqCs3BmZy1dIF7S7ldQtaGzPVB+nttpRKSQCpRISRUUwNK3oHZHSOzodhoJVKHhcPmfIH0TrJ7odhoRKaQCJVJSW2fC0R3O0gUi3tTsOqjVAeb9DXKz3E4jIqhAiZTckjehYm1oNsDtJBLojHE2Gj51AJaOdjuNiKACJVIyB9fArkVw6XAIDXM7jQSDpO7OKveLXoXMo26nEQl6KlAiJbF0NIRXcCb4ivhKn2fhzElY/IrbSUSCngqUSHGdSoW1H0Ob26B8FbfTSDCp1hza3ApLxzgLuIqIa1SgRIorZSzk52jyuLij51PO7by/uZtDJMipQIkUR242LBsLja+Eqg3cTiPBqHIiXHqvs6RB6nq304gELRUokeJY+zFkHobO97udRIJZ90cgsqKzuKaIuEIFSqSorIUf3oJqLaFesttpJJhViIUej8HWb2HnIrfTiAQlFSiRotq5ANLWO3OfjHE7jQS7TsOhYi1tNCziEhUokaJaMgqi4qHlILeTiEB4eej1e9i/HDZMdzuNSNBRgRIpisNbna1bOt4N4ZFupxFxtL4F4pvBnD9Dfq7baUSCipZQFimKpaMhNAI6DHM7ScCx1pKVm0/GmTwyz+RzOieP04W3/72fR2ZO/s+3GWfyyCz8ucycPKpVjOSyxvFc1iSehJggKrghoc7imhNvhhXjnIIvIj6hAiVyMZlHYdUEaHUTRCe4ncavZOfms2T7EeZsSmXfsayfC9HPJehMHpm5+UWewhMWYogqF0ZURCgVyoURVS6MCuGhLN15lC/XHASgRc2K9GwSz2WNE2hXpzJhoQE+0N64H9TpCvP/AZcMgXLRbicSCQoqUCIXs2Ic5GZq4cwiOnY6h7mb0pi1IZWFW9PJzMknKiKU+vHRRJULpUalSCpEhBFVLpSoiDCnCBUWouhyoc73IsKoUC6U6HJhVIgI/fl+ubDQcx7TWsuGgyeZvzmdBZvTGb1gB2/O205MZBg9GsXRs3ECyY3jqV4pAEenjIG+f4axfeCHUXDZE24nEgkKxvrw6o0OHTrYlJQUnx1PpNTyc+H11s6imUO/cDtNmbXr8Glmb0zl2w2ppOw6SoGFahXL0adZNfo2r0aXBlXPW3684URWLt9tO8yCzenM35JG6skzADStHkPPJgn0bBJP+7pVCA+k0anJt8P2efCbVRAd73YakYBgjFlure1wzu+pQIlcwNpPYOpdcMtkaHKl22nKjIICy6p9x5m1IZXZG1LZmpYBOAWlb3OnNLWqVQlTBpZ7sNay6dAp5m9OZ/7mNJbvPkZegSW6XBjdGlb9uVDVqFTe7ailc3grvHmpMw/q6hfdTiMSEFSgRErCWnj3csg6Dg+mQEgAjVaUQHZuPt9tO+yUpo1pHM44Q2iI4dJ6sfRtXo0+zaqRGFvB7ZgXdSo7l++2HWHBljTmb07n4IlsAJpUi3HmTjWJp0PdWCLC/PD9/uJhWPkRPPgjxNZ3O42I31OBEimJvT/C2L5w9T+h0z1up3HFkYwzzNmUxuwNqSzaepis3Hyiy4VxWZN4rmhejZ6NE6hUIdztmCVmrWVLagbzN6exYEs6y3YdJTffEhURSteGcfRsEk/PJgnUquwno1OnDsG/2kKTq2DQe26nEfF7FypQF51EboxJBD4AqgMFwBhr7evGmFhgMpAE7AJustYe81RoEdcteRMiKzlr7QSRHekZhaNMqSzffYwCCzUrRTK4Q236NKtG5/pV/XN05hyMMTSpHkOT6jHce1kDMs7k8f22w8zf4kxGn7UhFXBOTV7eLIHeTavRJrEyoSHun5o8p5jq0OUBWPgSdB0JNdu6nUgkYF10BMoYUwOoYa1dYYyJAZYDA4E7gaPW2heMMU8CVay1v7vQa2kESvzG8T3O5PGuI50rnALc3qOZTFq2h2/WHWJH+mnAWQ7gp0ngLWpWLBPzmXzJWsu2tAzmbU5jzsY0UnYfI7/AEhsVQc/G8fRu5lzZVzGyjI3AZZ90/t9NaO5c+BDkp55FSqNUI1DW2oPAwcKvTxljNgK1gAFAz8IfGwfMBy5YoET8xo9jAOPsNxag8gss8zal8dHS3SzYko4BujaIY2iXJPo0r+Y/p628xBhDo2oxNKoWw/DkBpzIzGXB1nTmbkxl7uY0pq3cT1iIoWNSbOHoVAL148vAGkyRFaHvc/D5SFj0Ty1rIOIlxZoDZYxJAhYCLYE91trKZ33vmLW2yjmeMxwYDlCnTp32u3fvLmVkES87kwGvNIeGl8Pg/7idxuPST51hSspeJizdw/7jWSTElGNIpzrc0inR/69E85G8/AJW7T3OnE1pzN2YxubUUwDUi4uid9MELm+aQIckFyeiWwuf3gtrpsAdn0KDXu7kEPFzHplEboyJBhYAf7XWTjPGHC9KgTqbTuGJX1j6NnzzBNw9B2qf83Pjd6y1LN15lI9+2M3M9YfIzbd0bVCV2zvXpW/zaoG1HpIL9h7N/PlU35IdR8jJKyCmXBg9GsfRu2k1ejaJJy66nG9D5ZyGd3rD6cMwYhFUrOnb44sEgFIXKGNMOPAlMNNa+0rhY5uBntbag4XzpOZba5tc6HVUoKTMKyiAN9pBVBzcPdvtNKV2MjuXacv3MX7pHramZVAxMoxB7RO5rXMdGpSF000BKDMnj++2HWHuplTmbEwj7dQZjIE2iZW5vKkzEb1ZjRjfzClL3wxjekGNS5z5UKFlbL6WSBlXqgJlnE/5OJwJ4w+f9fhLwJGzJpHHWmsveLJdBUrKvE1fw6RbYNB/oOUNbqcpsXX7T/DRD7uZvuoAWbn5tK5dids61+XaS2pSPsJ3K4IHu4ICZ4uZORvTmLspldX7TgBQo1IkvZomcEPbWnRIivVuiJ8Wg+06Eq74i3ePJRJgSlugugOLgLU4yxgA/B5YCkwB6gB7gMHW2qMXei0VKCnz3u8PR3fCQ6sh1L+2iszOzeeL1Qf4aOkeVu89TmR4CANa1+L2znVpVbuS2/EESDuVzfxN6czZlMrirYc5nZPPoPa1efrqZlSJivDegb96DJa9CzePh2b9vXcckQCjhTRFiuLgGni7h7NsQbeH3E5TZDsPn2b8D7v5ePk+TmTl0iA+its71+WGdrWpVF6nbMqqrJx8/j1vK28v2EHF8uH8sX8zBrap5Z1Te3ln4L1+cGQH3LsAYut5/hgiAUgFSqQoPr0PNkyHRzdA+cpup7mgvPwCZm9M5aMf9rB422HCQgz9WlTn9s516Vw/NujWbPJnmw6d5Klpa1m55zg9GsXxl4EtqVs1yvMHOrYb3k6GynXgrlkQHun5Y4gEGBUokYs5lQqvtYT2d8LVL7md5rwOnchm4o97mLRsD6knz1CzUiS3dKrDzR0TSaiovxD9VX6BZcLS3fxjxmZy8wt4uE9j7u5Rz/NXR26eARNvdv4/v/Z1z762SAAq1UKaIkEhZSzk58KlI9xOck4FBZb/fL+LF2dsIie/gORG8fxlYF16NYknTEsQ+L3QEMMdXZLo27w6z36+nn/M2MT0Vfv5+w2taFvngqvDFE+TK6H7I7D4VajTBVoP8dxriwQZjUCJ5GbDqy2gdke4dZLbaX5h95HT/PbjNfy46yi9mybwzLXNvXOKR8qMmesP8cz09aSeymZolyQeu6IxMZ7aMiY/Dz4YAAdWwD1zIaGZZ15XJABdaARK/3QVWTsFMg9Dl/vdTvL/FBRYPlyyi6teX8TGgyd5cdAljB3aQeUpCPRrUZ1ZjyYztEsS45bsou8rC/l2/SHPvHhoGAwaCxHRMPkOOHPKM68rEmRUoCS4WQs/vAXVWkJSD7fT/Gz/8SzueG8pf5y+nvZ1qzDjkWRu6pCoyeFBJCYynGeva8G0+7pSuUI4wz9czr0fpnDoRLYHXrw6DHoPjm6HLx5yPgciUiwqUBLcdsyHtA3Q+X4oA+XEWsuUZXvp9+pCVu45zl8GtuSDYZ2CfmPfYNa2ThW+GNmd313ZlPmb0+nzygI+XLKLgoJSlp56PaD3H2DdVGeNKBEpFhUoCW4/jIKoeGg1yO0kpJ7MZtj7y3hi6hpa1KzIjIeSub1zXY06CeGhIdzXswHfPpJM2zqV+eP09dw4+ns2HTpZuhfu9gg06gcznoL9yz0TViRIqEBJ8Dq8FbZ+Cx3vhjAfb/R6Fmstn63czxWvLmTJjiP8qX9zJt7TmTpVK7iWScqmulWj+GBYJ169uTW7j2TS/1+LeXHGJrJz80v2giEhcP1oiKkBU+6EzAtuJiEiZ1GBkuD1w1sQWg463OVahPRTZxjx0XIenryKBvFRfP2bHgzrXo+QEI06ybkZY7i+bW1mP3oZA9vWYtT87fR7bSHfbTtcshesEAs3vQ+nDsKnI5wNtUXkolSgJDhlHoXVE+GSwRAd70qEr9YcpN9rC5m3KZ2nrmrKxyO6Uj8+2pUs4n9ioyL45+DWTLj7Ugxw27tLeXTKKo6ezin+i9VqD1f+HbbOhO9e83RUkYCkAiXBacU4yM10Jo/72LHTOTw4YQUPTFhB7Srl+eo33bn3sgaEatRJSqBrwzhmPJzMg70a8vmqA1z+8nymLt9Hsdf463g3tLwR5j4POxd5J6xIANFCmhJ8so7DqC4Q1wiGfu7TQ8/akMpT09ZyIiuH3/RuxIieDTy/XYcErc2HTvHUtDWs2HOcLvWr8vQ1zWhZq1LRX+DMKRjTC7JPwIhFznIHIkFMC2mK/ORUKrzfH06nw2W/89lhT2Tl8uiUVdzzQQpx0RFMf6A7Iy9vpPIkHtWkegyfjOjK8wNbsunQSfq/sZiHJq1k79HMor1AuRi4+UPIyYBP7nJWLReRc9IIlASPY7vgg4GQkQo3fwQNL/fJYedvTuPJqWtJzzjD/T0bMLJ3IyLCVJzEu05m5/L2gu2MXbyTggK4vXNdRvZuSJWoiIs/efUk+PRe6P4o9HnG+2FFyqgLjUCpQElwSN0AH14Pedlw2yeQ2NHrh8w4k8dfv9rAxB/30jAhmpcHt6Z1YmWvH1fkbIdOZPPqrC18vHwvURFh3NerAcO61SMyPPTCT/ziIVj+Ptwy2dmEWCQIqUBJcNv7I4wfDOHl4Y5PfbJ56vfbD/Pbj9dw8EQW9yTX55E+jS/+F5aIF21JPcWLMzYxe2Ma1StG8ugVjbmxXe3zX7yQmw1j+8LxPXDvQqhS17eBRcoAFSgJXttmOxumxlSHOz7z+l8CWTn5/GPGJt7/fhf14qL45+DWtK9bxavHFCmOpTuO8LdvNrF673GaVIvhyaua0rNJ/LlXvD+6A97uCVXrw7CZri44K+IGTSKX4LRuKkwYAlUbOL/8vVye1u0/Qf83FvH+97u4s2sSX/+mh8qTlDmX1q/KZ/d3ZdRt7TiTl8+v31/GLe/8wOq9x3/5w7H1YeAoOLASZj7t86wiZZlGoCQwLXsXvnoc6nSBWydBZDEu5S6m/ALL6AXbeXXWFuKiy/HyTa3p1jDOa8cT8ZTc/AIm/riH12dv5cjpHK65pAZP9GtC3apR//8Hv/0DfP8G3Di2TOwbKeIrOoUnwcNaWPhPmPcXaHwlDH7fmfvkJXuPZvLolFUs23WMa1rV4K/Xt6RyhSJc5SRShpzKzuWdhTt4Z9FO8goKuO1S54q9qtGFp+zyc2HctXBwDQyfB/FN3A0s4iMqUBIcCgrg26fhh1Fwyc0w4E0IDffKoay1fLpyP3+avh6APw9owfVta517HomIn0g7mc1rc7YyedleyoeHMuKy+gzrXo8KEWFw8gCM7gFRcXDPXIiIuvgLivg5FSgJfPm58PlIZ3+7S++Dfn9zdpr3guOZOTz92Tq+WnOQTkmxvHxTaxJjK3jlWCJu2JaWwYszNvHthlQSYsrxaN/GDGpfm7BdC5zlQJpeA/1fhegEt6OKeJUKlAS23Cz4+New5Rvo9TQk/xa8NBK0eOthHv94NYczzvDoFY25N1l72EngStl1lL99vZEVe47TMCGa313ZlD7HJmNmPwOhEdD+Tuj2EFSs6XZUEa9QgZLAlX0CJt4Cu7+Hq1+CTvd45zC5+bw0czNjF++kQXwUr93clla1vTcxXaSssNYyc30qL87YxI7Dp+mUFMsfuoTTaudYzOrJEBIKbW+H7o9A5TpuxxXxKBUoCUwZ6fDRDZC2Aa5/22tXB208eJKHJ61ic+opftWlLk9d1YzyEVoUU4JLbn4Bk5ft5bXZWzmccYbG1aIZ1iKEgRlTiFw3EbDQeoiz/UvVBm7HFfEIFSgJPMd2O3MxTh5w9rVr1MfjhygosLz33U5enLGZiuXDeWnQJfRqqjkfEtwyc/L4fNUBJqfsZeWe44SHGgY3CuH+iC+pteNjTH4OtBwEyY/raj3xeypQEljSNjnlKfc03Pox1LnU44c4eCKLx6as5vvtR+jbvBov3NDqv5d0iwjgbA8zedlePl25n6Onc2hZMYtn4+bSLm0aIXnZ0HyAMyexeku3o4qUiAqUBI59KTB+kDOB9fZpXvnF/MXqAzz96VryCizPXNucmzokankCkQvIyStg9sZUJi/by8Kt6VSxJ3kmfh5XZ31JeN5paHKNMyJVq53bUUWKRQVKAsP2uTDpdoiOd/a1i63n0Zc/mZ3LM9PX8+nK/bRJrMxrN7chKU5r3YgUx4HjWXyyfB9TUvZy6lg6IyJncWfoDMrnn4KGfSD5Ca+MGot4gwqU+L/1n8HUuyGuMdwxzdkc2IOW7jjCo1NWc+hkNiN7N+TBXg0JC9VWkSIlVVBgWbLjCJOX7WXR+h3cYmdyb8Q3VLInyavbg7Cev4Ok7l5bckTEE7xWoIwxVwKvA6HAu9baFy708ypQUiLL34cvHobES5197cp7boPenLwCXpm1hbcXbqdObAVevbkN7epoA2ARTzqemcP0VQf47MettE//lHvDviTenOBkQgei+/6ekIa9VaSkTPJKgTLGhAJbgL7APmAZcIu1dsP5nqMCJcViLSx+FeY8Bw37wk0fQITnVvzelnaKhyatYv2BkwzpmMgf+zcnqlyYx15fRH5p3f4TTF26jYg1H3KnnU4Nc5RDMS0o1/tJqrS5VkVKyhRvFaguwLPW2n6F958CsNb+/XzP8XaB2r1pBWmbf/Da64tvVT22ivq7JrOn1jUsb/tXbIjn9rXbfyyLf8/bRlS5MF64oRVXtPDsKUERubDs3Hxmrt5N2qL/cOWxiSSGpLM7vAFpjW/16D+UJHDVan05NZO8u1TGhQpUaf65XQvYe9b9fcAvZgYaY4YDwwHq1PHuKrUHV3xF5y3/9OoxxLfG5fXl2e23YLefd2CzxHo2iefFQZeQEBPp8dcWkQuLDA9lQIf60OF5dqc9xtcz36Xl9nfpuP55t6OJn1ge/orXC9SFlGYEajDQz1p7d+H9O4BO1tqR53uOt0egThw7TMaxVK+9vviWDY0gP9o7e2yFhhhqVS6v5QlEypC83FxS924DCtyOIn6gcnwtomIqe/UY3hqB2gcknnW/NnCgFK9XapWqxFGpSpybEUREpITCwsOpVb+Z2zFEiqQ012kvAxoZY+oZYyKAIcDnnoklIiIiUnaVeATKWptnjHkQmImzjMF71tr1HksmIiIiUkaV6ppta+3XwNceyiIiIiLiF7TUsoiIiEgxqUCJiIiIFJNP98IzxqQDu4E44LDPDizepvczcOi9DBx6LwOH3kv31LXWxp/rGz4tUD8f1JiU862rIP5H72fg0HsZOPReBg69l2WTTuGJiIiIFJMKlIiIiEgxuVWgxrh0XPEOvZ+BQ+9l4NB7GTj0XpZBrsyBEhEREfFnOoUnIiIiUkw+L1DGmCuNMZuNMduMMU/6+vjiOcaYXcaYtcaYVcaYFLfzSPEYY94zxqQZY9ad9VisMWaWMWZr4W0VNzNK0ZznvXzWGLO/8PO5yhhztZsZpWiMMYnGmHnGmI3GmPXGmIcKH9dns4zxaYEyxoQCbwJXAc2BW4wxzX2ZQTyul7W2jS6x9UvvA1f+z2NPAnOstY2AOYX3pex7n1++lwCvFn4+2xRuvSVlXx7wmLW2GdAZeKDw70l9NssYX49AdQK2WWt3WGtzgEnAAB9nEBHAWrsQOPo/Dw8AxhV+PQ4Y6MtMUjLneS/FD1lrD1prVxR+fQrYCNRCn80yx9cFqhaw96z7+wofE/9kgW+NMcuNMcPdDiMeUc1aexCcX+RAgst5pHQeNMasKTzFp1M+fsYYkwS0BZaiz2aZ4+sCZc7xmC4D9F/drLXtcE7JPmCMSXY7kIj87C2gAdAGOAi87GoaKRZjTDQwFXjYWnvS7TzyS74uUPuAxLPu1wYO+DiDeIi19kDhbRrwKc4pWvFvqcaYGgCFt2ku55ESstamWmvzrbUFwDvo8+k3jDHhOOVpvLV2WuHD+myWMb4uUMuARsaYesaYCGAI8LmPM4gHGGOijDExP30NXAGsu/CzxA98Dgwt/HooMN3FLFIKP/1lW+h69Pn0C8YYA4wFNlprXznrW/psljE+X0iz8FLa14BQ4D1r7V99GkA8whhTH2fUCSAMmKD30r8YYyYCPXF2ek8FngE+A6YAdYA9wGBrrSYnl3HneS974py+s8Au4N6f5tBI2WWM6Q4sAtYCBYUP/x5nHpQ+m2WIViIXERERKSatRC4iIiJSTCpQIiIiIsWkAiUiIiJSTCpQIiIiIsWkAiUiIiJSTCpQIuJ3jDHfn+fx940xg3ydR0SCjwqUiPgda21XtzOISHALczuAiEhxGWMyrLXRhas2vwH0BnZy7v02RUQ8TiNQIuLPrgeaAK2AewCNTImIT6hAiYg/SwYmFm6aewCY63YgEQkOKlAi4u+0H5WI+JwKlIj4s4XAEGNMqDGmBtDL7UAiEhw0iVxE/NmnOBPI1wJbgAXuxhGRYGGs1ei3iIiISHHoFJ6IiIhIMalAiYiIiBSTCpSIiIhIMalAiYiIiBSTCpSIiIhIMalAiYiIiBSTCpSIiIhIMalAiYiIiBTT/wHQrO/TEeUKXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['p','fdgl'],figsize=(10,3),kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.发电功率与q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['q','fdgl'],figsize=(10,3),kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.发电功率的周期性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f369302b48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 10800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "file_dir = './data/all_train_data.csv'\n",
    "all_data_frame = pd.read_csv(file_dir)\n",
    "all_data_frame = all_data_frame[all_data_frame['ycsb']=='202106030258GF']\n",
    "all_data_frame.plot(x='data_date',y=['fdgl'],figsize=(150,10),kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "file_dir = './data/train_data_1.csv'\n",
    "all_data_frame = pd.read_csv(file_dir)\n",
    "#all_data_frame = all_data_frame[all_data_frame['ycsb']=='202106300288GF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = ['fdgl','low_tp', 'high_tp', 'avg_tp', 'wind']\n",
    "all_data_frame = all_data_frame[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_frame = np.array(all_data_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  1. , 10. ,  5.5,  3.5],\n",
       "       [ 0. ,  1. , 10. ,  5.5,  3.5],\n",
       "       [ 0. ,  1. , 10. ,  5.5,  3.5],\n",
       "       ...,\n",
       "       [ 0. , -3. ,  7. ,  2. ,  3.5],\n",
       "       [ 0. , -3. ,  7. ,  2. ,  3.5],\n",
       "       [ 0. , -3. ,  7. ,  2. ,  3.5]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fdgl', 'low_tp', 'high_tp', 'avg_tp', 'wind']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "['cur_a',\n",
    " 'cur_b',\n",
    " 'cur_c',\n",
    " 'p',\n",
    " 'szgl',\n",
    " '净日照强度(net,J/m2)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4\n",
       "0  1.00 -0.10 -0.09 -0.10  0.02\n",
       "1 -0.10  1.00  0.89  0.97 -0.47\n",
       "2 -0.09  0.89  1.00  0.97 -0.42\n",
       "3 -0.10  0.97  0.97  1.00 -0.46\n",
       "4  0.02 -0.47 -0.42 -0.46  1.00"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coorelation_matrix=np.corrcoef(all_data_frame,rowvar=0)  #相关性分析,rowvar=0表示对列进行分析\n",
    "pd.DataFrame(coorelation_matrix.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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